<p>This course introduces you to sampling and exploring data, as well as basic probability theory. You will examine various types of sampling methods and discuss how such methods can impact the utility of a data analysis. The concepts in this module will serve as building blocks for our later courses.<p>Each lesson comes with a set of learning objectives that will be covered in a series of short videos. Supplementary readings and practice problems will also be suggested from <a href="https://leanpub.com/openintro-statistics/" target="_blank">OpenIntro Statistics, 3rd Edition</a> (a free online introductory statistics textbook, that I co-authored). There will be weekly quizzes designed to assess your learning and mastery of the material covered that week in the videos. In addition, each week will also feature a lab assignment, in which you will use R to apply what you are learning to real data. There will also be a data analysis project designed to enable you to answer research questions of your own choosing.<p>Since this is a Coursera course, you are welcome to participate as much or as little as you’d like, though I hope that you will begin by participating fully. One of the most rewarding aspects of a Coursera course is participation in forum discussions about the course materials. Please take advantage of other students' feedback and insight and contribute your own perspective where you see fit to do so. You can also check out the <a href="https://www.coursera.org/learn/probability-intro/resources/crMc4" target="_blank">resource page</a> listing useful resources for this course. <p>Thank you for joining the Introduction to Probability and Data community! Say hello in the Discussion Forums. We are looking forward to your participation in the course.</p>

BS

I would give it 5 stars if it was truly for zero beginners. I myself didn't have much problem understanding the content, but I can imagine that people with no background in statistics would have a very hard time. Some very important concepts are just glossed over. Another problem is that much more attention is given to the mathematics behind the stats, as opposed to how to conduct the tests themselves. I'm finishing the 2nd course two (inferential statistics) and I have the same feeling there. We spend video after video learning the nuts and bolts of the math behind, but at times we are only given the code in R. Sometimes the code is not even given!! The same with ggplots. The real-life applicability of the knowledge here is to make sure you are able to use the software to run the analysis. It's important to understand the logic behind, surely, but no one will, professionally, do the calculations by hand. I left this and will leave the second course (Inferential statistics) with the feeling that I've learnt much more the maths than how to actually use R.

One final thought: at times, in stats, the most difficult thing is to decide which test to implement. There are possibilities, but which one? Why? How to I check for Skeweness in R (the number, not the histogram). What is considered too much skewness? What is too large a bias in bootstrapping? These are just examples of precious, directly applicable information that's left out